Abstract: Systems and methods are provided that may be implemented to automatically discover intended and/or unintended changes as they occur in an IT environment, such as a network that includes multiple endpoint information handling systems. Intended changes may include, but are not limited to, changes in user and device or endpoint system level experience metrics that occur on one or more of the endpoint information handling systems within the IT environment after a planned action occurring in the IT environment. Unintended changes to similar metrics may occur when no activities are planned or initiated for the IT environment. These IT environment changes may be discovered automatically based on individual device level telemetry data and/or fleetwide aggregations derived of benchmarking various telemetry metrics.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive a first multiple telemetry data from multiple information handling systems (IHSs); determine first multiple performance health scores respectively associated with the IHSs; determine first multiple availability scores respectively associated with the IHSs; determine first multiple information handling system (IHS) update churn scores respectively associated with the IHSs; determine, via a machine learning process, a second distribution of the IHSs to the multiple IHS groups based at least on the first multiple IHS and performance health scores, the first multiple availability scores, and the first multiple IHS churn scores; and provide a first software update to IHSs of each IHS group.

Abstract: In one or more embodiments, one or more systems, one or more methods, and/or one or more processes may: receive a first multiple telemetry data from multiple information handling systems (IHSs); determine first multiple performance health scores respectively associated with the IHSs; determine first multiple availability scores respectively associated with the IHSs; determine first multiple information handling system (IHS) update churn scores respectively associated with the IHSs; determine, via a machine learning process, a second distribution of the IHSs to the multiple IHS groups based at least on the first multiple IHS and performance health scores, the first multiple availability scores, and the first multiple IHS churn scores; and provide a first software update to IHSs of each IHS group.

Abstract: Computer programs for execution on an information handling system may be illustrated using an ontological mapping that uses data regarding the computer programs to arrange the computer programs into a navigable display that improves the ability of a user to find organize and find their programs and allows the information handling system to make useful recommendations to the user regarding additional computer programs of interest. The ontologically-mapped programs may be visualized on a display that is customizable based on a user's interests. For example, programs may be mapped and visualized as a universe by grouping related programs into solar systems. Data regarding the programs may be used to generate details within the solar system, such as appearance of the planets in the solar systems, location of the planets in the solar systems, and moons orbiting the planets in the solar systems.

Abstract: An organic light-emitting apparatus includes an organic light-emitting device and a magnetic field-applying member that applies a magnetic field to the organic light-emitting device. The organic light-emitting device includes a host and a dopant.

Abstract: An organic light-emitting apparatus includes an organic light-emitting device and a magnetic field-applying member that applies a magnetic field to the organic light-emitting device. The organic light-emitting device includes a host and a dopant.